Identifying student help-seeking behavior patterns and help-seeking tendencies from student problem-solving and help-seeking behavior data: An educational data mining approach
Studies have shown that students have different help-seeking behavior patterns and tendencies and furthermore, that students with certain help-seeking behavior patterns and tendencies may have poor performance (i.e., at-risk students). This study applied an educational data mining approach, includin...
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| Format: | Article |
| Language: | English |
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International Forum of Educational Technology & Society
2025-04-01
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| Series: | Educational Technology & Society |
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| Online Access: | https://www.j-ets.net/collection/published-issues/28_2#h.hmuyxcdixa8o |
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| author | Chih-Yueh Chou, Wei-Han Chen |
| author_facet | Chih-Yueh Chou, Wei-Han Chen |
| author_sort | Chih-Yueh Chou, Wei-Han Chen |
| collection | DOAJ |
| description | Studies have shown that students have different help-seeking behavior patterns and tendencies and furthermore, that students with certain help-seeking behavior patterns and tendencies may have poor performance (i.e., at-risk students). This study applied an educational data mining approach, including clustering and classification, to analyze students’ problem-solving and help-seeking data in a computer assisted learning system to identify student help-seeking behavior patterns and tendencies. First, nine observable problem-solving and help-seeking features for identifying help-seeking behavior patterns were established. Second, this study applied the k-means clustering method and identified three well-known help-seeking behavior patterns: executive, avoidant, and instrumental help-seeking. The results further identified two new help-seeking behavior patterns. One was static instrumental help-seeking and the other was static instrumental and executive help-seeking. Third, executive help-seeking and static instrumental and executive help-seeking patterns could be used as at-risk predicators of poor performance. Fourth, the study applied clustered and identified results to build a minimum distance classifier to identify help-seeking behavior patterns in new data. The study also investigated the accuracy of the classifier in early identifying help-seeking behavior patterns from early-stage data. The early identification accuracy was 61% for the first three minutes and 75% for the seven-minutes of early-stage data, respectively. Fifth, this study identified three help-seeking tendencies: independent problem-solvers, executive help-seekers, and static instrumental and executive help-seekers. In summary, the study showed the feasibility and effectiveness of applying an educational data mining approach, including clustering and classification, to build data-driven student models to identify student help-seeking behavior patterns and tendencies. |
| format | Article |
| id | doaj-art-2687fca34eb44633b8bd7dff99b05897 |
| institution | OA Journals |
| issn | 1176-3647 1436-4522 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | International Forum of Educational Technology & Society |
| record_format | Article |
| series | Educational Technology & Society |
| spelling | doaj-art-2687fca34eb44633b8bd7dff99b058972025-08-20T02:17:34ZengInternational Forum of Educational Technology & SocietyEducational Technology & Society1176-36471436-45222025-04-0128294110https://doi.org/10.30191/ETS.202504_28(2).RP06Identifying student help-seeking behavior patterns and help-seeking tendencies from student problem-solving and help-seeking behavior data: An educational data mining approachChih-Yueh Chou, Wei-Han ChenStudies have shown that students have different help-seeking behavior patterns and tendencies and furthermore, that students with certain help-seeking behavior patterns and tendencies may have poor performance (i.e., at-risk students). This study applied an educational data mining approach, including clustering and classification, to analyze students’ problem-solving and help-seeking data in a computer assisted learning system to identify student help-seeking behavior patterns and tendencies. First, nine observable problem-solving and help-seeking features for identifying help-seeking behavior patterns were established. Second, this study applied the k-means clustering method and identified three well-known help-seeking behavior patterns: executive, avoidant, and instrumental help-seeking. The results further identified two new help-seeking behavior patterns. One was static instrumental help-seeking and the other was static instrumental and executive help-seeking. Third, executive help-seeking and static instrumental and executive help-seeking patterns could be used as at-risk predicators of poor performance. Fourth, the study applied clustered and identified results to build a minimum distance classifier to identify help-seeking behavior patterns in new data. The study also investigated the accuracy of the classifier in early identifying help-seeking behavior patterns from early-stage data. The early identification accuracy was 61% for the first three minutes and 75% for the seven-minutes of early-stage data, respectively. Fifth, this study identified three help-seeking tendencies: independent problem-solvers, executive help-seekers, and static instrumental and executive help-seekers. In summary, the study showed the feasibility and effectiveness of applying an educational data mining approach, including clustering and classification, to build data-driven student models to identify student help-seeking behavior patterns and tendencies.https://www.j-ets.net/collection/published-issues/28_2#h.hmuyxcdixa8ohelp-seeking behaviors and tendencieseducational data miningclusteringclassificationdata-driven student model |
| spellingShingle | Chih-Yueh Chou, Wei-Han Chen Identifying student help-seeking behavior patterns and help-seeking tendencies from student problem-solving and help-seeking behavior data: An educational data mining approach Educational Technology & Society help-seeking behaviors and tendencies educational data mining clustering classification data-driven student model |
| title | Identifying student help-seeking behavior patterns and help-seeking tendencies from student problem-solving and help-seeking behavior data: An educational data mining approach |
| title_full | Identifying student help-seeking behavior patterns and help-seeking tendencies from student problem-solving and help-seeking behavior data: An educational data mining approach |
| title_fullStr | Identifying student help-seeking behavior patterns and help-seeking tendencies from student problem-solving and help-seeking behavior data: An educational data mining approach |
| title_full_unstemmed | Identifying student help-seeking behavior patterns and help-seeking tendencies from student problem-solving and help-seeking behavior data: An educational data mining approach |
| title_short | Identifying student help-seeking behavior patterns and help-seeking tendencies from student problem-solving and help-seeking behavior data: An educational data mining approach |
| title_sort | identifying student help seeking behavior patterns and help seeking tendencies from student problem solving and help seeking behavior data an educational data mining approach |
| topic | help-seeking behaviors and tendencies educational data mining clustering classification data-driven student model |
| url | https://www.j-ets.net/collection/published-issues/28_2#h.hmuyxcdixa8o |
| work_keys_str_mv | AT chihyuehchouweihanchen identifyingstudenthelpseekingbehaviorpatternsandhelpseekingtendenciesfromstudentproblemsolvingandhelpseekingbehaviordataaneducationaldataminingapproach |